Derived distribution of annual precipitation: Effects of record length and rainfall data aggregation
Claudio Meier, Jorge Sebastián Moraga, Geri Pranzini, Peter Molnár
Tuesday 30 june 2015
8:30 - 8:45h
at North America (level 0)
Themes: (T) Extreme events, natural variability and climate change, (ST) Hydrological extremes: floods and droughts
Parallel session: 4I. Extreme events – Flood Drought
The interannual variability of precipitation is given by the probability distribution (pdf) of annual rainfall, which in temperate zones is typically obtained by fitting a normal to a series of annual rainfall data. There are three problems with this approach: (i) a long record (n > 25 ~ 30) is required in order to fit the model; (ii) years with missing data cannot be used for the analysis; and (iii) non-homogeneities can happen over the period required for adequately fitting a probabilistic model. Eagleson (1978) proposed using derived distributions to obtain the pdf of annual rainfall, by combining the marginal pdfs for storm depth and inter-arrival time. Our aim was to assess the differences between Eagleson´s approach and the fitting of a normal, looking at two effects: length of the record and level of aggregation of the rainfall data. We quantified the first effect by randomly subsampling the rainfall series, in order to create multiple synthetic records of different lengths, to which we applied both methods. We then repeated the analyses for data aggregated over 12 and 24-h long periods, the typical information available at most weather stations. We used data from Concepción, Chile (25 years), and Lugano, Switzerland (32 years), to compare two different climates. Both approaches yield very similar pdfs when using all the data available. In Concepción, totalizing rainfall every 12 h results in a pdf that is undistinguishable from that obtained for continuous data, but aggregating over 24 h introduces bias, while in Lugano any aggregation introduces a bias. The proposed method is a much more consistent way of estimating the pdf of annual rainfall with only a few years of data, allowing for a robust estimation even for records as short as 5 years. Thus, derived distributions are a powerful tool for describing interannual variability in rainfall, in the case of short records or when the climate might be non-stationary.
